用外部知识提升大模型时间序列预测能力
Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
- 将结构化时间信息注入大模型，增强其对时序数据的理解
- 实测显示新方法在真实数据集上显著优于无辅助信息的基线
- 适合需要高精度预测的能源、金融等领域的研究者
随着大型语言模型（LLMs）的广泛应用，亟需建立超越传统自然语言任务的最佳实践。本文提出一种跨领域知识迁移框架，以提升LLMs在时间序列预测中的表现——这一任务在能源系统、金融和医疗等领域日益重要。该方法系统性地向LLMs注入结构化的时间信息，从而提升其预测准确性。研究在真实世界的时间序列数据集上评估了该方法，并与未接收任何辅助信息的基线进行对比。结果表明，引入知识的预测方法在预测准确性和泛化能力上均显著优于无知识的基线。这些发现凸显了知识迁移策略在弥合大模型与特定领域预测任务之间差距方面的潜力。
原文摘要 · Abstract (English)
With the widespread adoption of Large Language Models (LLMs), there is a growing need to establish best practices for leveraging their capabilities beyond traditional natural language tasks. In this paper, a novel cross-domain knowledge transfer framework is proposed to enhance the performance of LLMs in time series forecasting -- a task of increasing relevance in fields such as energy systems, finance, and healthcare. The approach systematically infuses LLMs with structured temporal information to improve their forecasting accuracy. This study evaluates the proposed method on a real-world time series dataset and compares it to a naive baseline where the LLM receives no auxiliary information. Results show that knowledge-informed forecasting significantly outperforms the uninformed baseline in terms of predictive accuracy and generalization. These findings highlight the potential of knowledge transfer strategies to bridge the gap between LLMs and domain-specific forecasting tasks.
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